Aligning faces with py opencv-dlib combo

Face alignment with Dlib and OpenCV

This is my first trial at using Jupyter notebook to write a post, hope it makes sense.

I've recently taught a class on generative models: http://hi.cs.stonybrook.edu/teaching/cdt450

In class we've manipulated face images with neural networks.

One important thing I found that helped is to align the images so the facial features overlap.
It helps the nets learn the variance in faces better, rather than waste their "representation power" on the shift between faces.

The following is some code to align face images using the excellent Dlib (python bindings) http://dlib.net. First I'm just using a standard face detector, and then using the facial fatures extractor I'm using that information for a complete alignment of the face.

After the alignment - I'm just having fun with the aligned dataset 🙂
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Build your AWS Lambda Machine Learning Function with Docker

I've recently made a tutorial on using Docker for machine learning purposes, and I thought also to publish it in here: http://hi.cs.stonybrook.edu/teaching/docker4ml

It includes videos, slides and code, with hands-on demonstrations in class.

A GitHub repo holds the code: https://github.com/royshil/Docker4MLTutorial

I made several scripts to make it easy to upload python code that performs an ML inference ("prediction") operation on AWS Lambda.

Enjoy!
Roy.

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An automatic Tensorflow-CUDA-Docker-Jupyter machine on Google Cloud Platform


For a class I'm teaching (on deep learning and art) I had to create a machine that auto starts a jupyter notebook with tensorflow and GPU support. Just create an instance and presto - Jupyter notebook with TF and GPU!
How awesome is that?

Well... building it wasn't that simple.
So for your enjoyment - here's my recipe:
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Projector-Camera Calibration - the "easy" way

First let me open by saying projector-camera calibration is NOT EASY. But it's technically not complicated too.

It is however, an amalgamation of optimizations that accrue and accumulate error with each step, so that the end product is not far from a random guess.
So 3D reconstructions I was able to get from my calibrated pro-cam were just a distorted mess of points.

Nevertheless, here come the deets.
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Revisiting graph-cut segmentation with SLIC and color histograms [w/Python]

As part of the computer vision class I'm teaching at SBU I asked students to implement a segmentation method based on SLIC superpixels. Here is my boilerplate implementation.

This follows the work I've done a very long time ago (2010) on the same subject.

For graph-cut I've used PyMaxflow: https://github.com/pmneila/PyMaxflow, which is very easily installed by just pip install PyMaxflow

The method is simple:

  • Calculate SLIC superpixels (the SKImage implementation)
  • Use markings to determine the foreground and background color histograms (from the superpixels under the markings)
  • Setup a graph with a straightforward energy model: Smoothness term = K-L-Div between superpix histogram and neighbor superpix histogram, and Match term = inf if marked as BG or FG, or K-L-Div between SuperPix histogram and FG and BG.
  • To find neighbors I've used Delaunay tessellation (from scipy.spatial), for simplicity. But a full neighbor finding could be implemented by looking at all the neighbors on the superpix's boundary.
  • Color histograms are 2D over H-S (from the HSV)

Result

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[Python] OpenCV capturing from a v4l2 device

I tried to set the capture format on a webcam from OpenCV's cv2.VideoCapture and ran into a problem: it's using the wrong IOCTL command.
So I used python-v4l2capture to get images from the device, which allows more control.
Here is the gist:

Enjoy!
Roy

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OpenCV Python YAML persistance

I wasn't able to find online a complete example on how to persist OpenCV matrices in Python (so really NumPy arrays) to YAML like what cv::FileStorage will give you.

So here's a short snippet:

import numpy as np
import yaml

# A yaml constructor is for loading from a yaml node.
# This is taken from: http://stackoverflow.com/a/15942429
def opencv_matrix_constructor(loader, node):
    mapping = loader.construct_mapping(node, deep=True)
    mat = np.array(mapping["data"])
    mat.resize(mapping["rows"], mapping["cols"])
    return mat
yaml.add_constructor(u"tag:yaml.org,2002:opencv-matrix", opencv_matrix_constructor)

# A yaml representer is for dumping structs into a yaml node.
# So for an opencv_matrix type (to be compatible with c++'s FileStorage) we save the rows, cols, type and flattened-data
def opencv_matrix_representer(dumper, mat):
    mapping = {'rows': mat.shape[0], 'cols': mat.shape[1], 'dt': 'd', 'data': mat.reshape(-1).tolist()}
    return dumper.represent_mapping(u"tag:yaml.org,2002:opencv-matrix", mapping)
yaml.add_representer(np.ndarray, opencv_matrix_representer)


#example
with open('output.yaml', 'w') as f:
    f.write("%YAML:1.0")
    yaml.dump({"a matrix": np.zeros((10,10)), "another_one": np.zeros((2,4))}, f)

#   a matrix: !!opencv-matrix
#     cols: 10
#     data: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
#       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
#       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
#       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
#       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
#       0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
#       0.0, 0.0, 0.0, 0.0, 0.0]
#     dt: d
#     rows: 10
#   another_one: !!opencv-matrix
#     cols: 4
#     data: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
#     dt: d
#     rows: 2

with open('output.yaml', 'r') as f:
    print yaml.load(f)
  
#  {'a matrix': array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
#         [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
#         [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
#         [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
#         [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
#         [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
#         [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
#         [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
#         [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
#         [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]]), 'another_one': array([[ 0.,  0.,  0.,  0.],
#         [ 0.,  0.,  0.,  0.]])}

There you go

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GDoc/LaTeX compilation GUI with Tkinter/Python [w/ code]

¡Hola mis amigos!
I'm learning spanish, but I'm also annoyed with collaborating on LaTeX papers. That's why I've created the GDoc-LaTeXifier so the syntax will be clear when I collaborate on a paper with a remote friend.
But now we both want to compile a PDF on our machines. So I've created the tiny shell script that downloads the paper and runs PDFLaTeX.
The problem is that this opens a new terminal window and runs the script. I've been able to sort it out so that it closes the terminal window when it's done, by on my friend's mac it doesn't, so he ends up with a ton of open windows.
Enter - the GDoc/LaTeX compiler GUI.
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Getting all the links from a MediaWiki format using PyParsing

Hi,
Just sharing a snippet of code. Part of a project I'm doing, I need to analyse the links in the Wikipedia corpus. While using the API is one solution, it doesn't retain the order of where links appear in the page. It also returns links that are not part of the main text, which makes the linkage DB very cluttered.
So, I set out to parse the raw MediaWiki format all Wikipedia articles are written in, to get only the relevant links and in order. I call them contextual because they live inside the text and have context.
Initially I used string matching, and other complex string scraping parsing methods. It was a bust. There are too many end-cases to deal with. That is when I discovered PyParsing, the excellent parsing library. It did the job, and here are the results.
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